
"""simple docstring"""





import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard



@pytest.mark.parametrize(
    """compression_format, is_archive"""    ,							[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]    ,							)
def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							):
		__SCREAMING_SNAKE_CASE				=					{
		    """7z""": (seven_zip_file, SevenZipExtractor),
		    """bz2""": (bza_file, BzipaExtractor),
		    """gzip""": (gz_file, GzipExtractor),
		    """lz4""": (lza_file, LzaExtractor),
		    """tar""": (tar_file, TarExtractor),
		    """xz""": (xz_file, XzExtractor),
		    """zip""": (zip_file, ZipExtractor),
		    """zstd""": (zstd_file, ZstdExtractor),
		}
		__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE				=					input_paths_and_base_extractors[compression_format]
		if input_path is None:
				__SCREAMING_SNAKE_CASE				=					f"for '{compression_format}' compression_format, "
				if compression_format == "7z":
						reason += require_pyazr.kwargs["reason"]
				elif compression_format == "lz4":
						reason += require_lza.kwargs["reason"]
				elif compression_format == "zstd":
						reason += require_zstandard.kwargs["reason"]
				pytest.skip(__snake_case							)
		assert base_extractor.is_extractable(__snake_case							)
		__SCREAMING_SNAKE_CASE				=					tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
		base_extractor.extract(__snake_case    ,							__snake_case							)
		if is_archive:
				assert output_path.is_dir()
				for file_path in output_path.iterdir():
						assert file_path.name == text_file.name
						__SCREAMING_SNAKE_CASE				=					file_path.read_text(encoding="""utf-8"""							)
		else:
				__SCREAMING_SNAKE_CASE				=					output_path.read_text(encoding="""utf-8"""							)
		__SCREAMING_SNAKE_CASE				=					text_file.read_text(encoding="""utf-8"""							)
		assert extracted_file_content == expected_file_content



@pytest.mark.parametrize(
    """compression_format, is_archive"""    ,							[
        ("""7z""", True),
        ("""bz2""", False),
        ("""gzip""", False),
        ("""lz4""", False),
        ("""tar""", True),
        ("""xz""", False),
        ("""zip""", True),
        ("""zstd""", False),
    ]    ,							)
def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							):
		__SCREAMING_SNAKE_CASE				=					{
		    """7z""": seven_zip_file,
		    """bz2""": bza_file,
		    """gzip""": gz_file,
		    """lz4""": lza_file,
		    """tar""": tar_file,
		    """xz""": xz_file,
		    """zip""": zip_file,
		    """zstd""": zstd_file,
		}
		__SCREAMING_SNAKE_CASE				=					input_paths[compression_format]
		if input_path is None:
				__SCREAMING_SNAKE_CASE				=					f"for '{compression_format}' compression_format, "
				if compression_format == "7z":
						reason += require_pyazr.kwargs["reason"]
				elif compression_format == "lz4":
						reason += require_lza.kwargs["reason"]
				elif compression_format == "zstd":
						reason += require_zstandard.kwargs["reason"]
				pytest.skip(__snake_case							)
		__SCREAMING_SNAKE_CASE				=					Extractor.infer_extractor_format(__snake_case							)
		assert extractor_format is not None
		__SCREAMING_SNAKE_CASE				=					tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
		Extractor.extract(__snake_case    ,							__snake_case    ,							__snake_case							)
		if is_archive:
				assert output_path.is_dir()
				for file_path in output_path.iterdir():
						assert file_path.name == text_file.name
						__SCREAMING_SNAKE_CASE				=					file_path.read_text(encoding="""utf-8"""							)
		else:
				__SCREAMING_SNAKE_CASE				=					output_path.read_text(encoding="""utf-8"""							)
		__SCREAMING_SNAKE_CASE				=					text_file.read_text(encoding="""utf-8"""							)
		assert extracted_file_content == expected_file_content



@pytest.fixture
def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_							):
		import tarfile

		__SCREAMING_SNAKE_CASE				=					tmp_path / """data_dot_dot"""
		directory.mkdir()
		__SCREAMING_SNAKE_CASE				=					directory / """tar_file_with_dot_dot.tar"""
		with tarfile.TarFile(__snake_case    ,							"""w"""							) as f:
				f.add(__snake_case    ,							arcname=os.path.join(""".."""    ,							text_file.name							)							)
		return path



@pytest.fixture
def 		_lowerCAmelCase							(							UpperCamelCase_							):
		import tarfile

		__SCREAMING_SNAKE_CASE				=					tmp_path / """data_sym_link"""
		directory.mkdir()
		__SCREAMING_SNAKE_CASE				=					directory / """tar_file_with_sym_link.tar"""
		os.symlink(""".."""    ,							directory / """subdir"""    ,							target_is_directory=__snake_case							)
		with tarfile.TarFile(__snake_case    ,							"""w"""							) as f:
				f.add(str(directory / """subdir"""							)    ,							arcname="""subdir"""							)  # str required by os.readlink on Windows and Python < 3.8
		return path



@pytest.mark.parametrize(
    """insecure_tar_file, error_log"""    ,							[("""tar_file_with_dot_dot""", """illegal path"""), ("""tar_file_with_sym_link""", """Symlink""")]    ,							)
def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					{
		    """tar_file_with_dot_dot""": tar_file_with_dot_dot,
		    """tar_file_with_sym_link""": tar_file_with_sym_link,
		}
		__SCREAMING_SNAKE_CASE				=					insecure_tar_files[insecure_tar_file]
		__SCREAMING_SNAKE_CASE				=					tmp_path / """extracted"""
		TarExtractor.extract(__snake_case    ,							__snake_case							)
		assert caplog.text
		for record in caplog.records:
				assert record.levelname == "ERROR"
				assert error_log in record.msg



def 		_lowerCAmelCase							(							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					tmpdir / """not_a_zip_file"""
		# From: https://github.com/python/cpython/pull/5053
		__SCREAMING_SNAKE_CASE				=					(
		    b"""\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"""
		    b"""\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I"""
		    b"""DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"""
		    b"""\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"""
		)
		with not_a_zip_file.open("""wb"""							) as f:
				f.write(__snake_case							)
		assert zipfile.is_zipfile(str(__snake_case							)							)  # is a false positive for `zipfile`
		assert not ZipExtractor.is_extractable(__snake_case							)  # but we're right












"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case : Dict     		=		logging.get_logger(__name__)



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""input_values""", """padding_mask"""]







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int = 1				,    UpperCamelCase_ :	int = 2_4_0_0_0				,    UpperCamelCase_ :	float = 0.0				,    UpperCamelCase_ :	float = None				,    UpperCamelCase_ :	float = None				,    **UpperCamelCase_ :	str				,    ):





           '''simple docstring'''

           super().__init__(feature_size=UpperCamelCase_				,    sampling_rate=UpperCamelCase_				,    padding_value=UpperCamelCase_				,    **UpperCamelCase_ )
           __magic_name__      				=    chunk_length_s
           __magic_name__      				=    overlap







    @property
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.chunk_length_s is None:
                  return None
           else:
                  return int(self.chunk_length_s * self.sampling_rate )







    @property
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
                  return None
           else:
                  return max(1				,    int((1.0 - self.overlap) * self.chunk_length ) )







    def __call__(		self :	List[Any]				,    UpperCamelCase_ :	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,    UpperCamelCase_ :	Optional[Union[bool, str, PaddingStrategy]] = None				,    UpperCamelCase_ :	Optional[bool] = False				,    UpperCamelCase_ :	Optional[int] = None				,    UpperCamelCase_ :	Optional[Union[str, TensorType]] = None				,    UpperCamelCase_ :	Optional[int] = None				,    ):





           '''simple docstring'''

           if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                             f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                             f""" {self.sampling_rate} and not {sampling_rate}.""" )
           else:
                  logger.warning(
                      'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                      'Failing to do so can result in silent errors that might be hard to debug.' )

           if padding and truncation:
                  raise ValueError('Both padding and truncation were set. Make sure you only set one.' )
           elif padding is None:
                  # by default let's pad the inputs
                  __magic_name__      				=    True

           __magic_name__      				=    bool(
               isinstance(UpperCamelCase_				,    (list, tuple) ) and (isinstance(raw_audio[0]				,    (np.ndarray, tuple, list) )) )

           if is_batched:
                  __magic_name__      				=    [np.asarray(UpperCamelCase_				,    dtype=np.floataa ).T for audio in raw_audio]
           elif not is_batched and not isinstance(UpperCamelCase_				,    np.ndarray ):
                  __magic_name__      				=    np.asarray(UpperCamelCase_				,    dtype=np.floataa )
           elif isinstance(UpperCamelCase_				,    np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
                  __magic_name__      				=    raw_audio.astype(np.floataa )

           # always return batch
           if not is_batched:
                  __magic_name__      				=    [np.asarray(UpperCamelCase_ ).T]

           # verify inputs are valid
           for idx, example in enumerate(UpperCamelCase_ ):
                  if example.ndim > 2:
                         raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}""" )
                  if self.feature_size == 1 and example.ndim != 1:
                         raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels""" )
                  if self.feature_size == 2 and example.shape[-1] != 2:
                         raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels""" )

           __magic_name__      				=    None
           __magic_name__      				=    BatchFeature({'input_values': raw_audio} )
           if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                  if truncation:
                         __magic_name__      				=    min(array.shape[0] for array in raw_audio )
                         __magic_name__      				=    int(np.floor(max_length / self.chunk_stride ) )
                         __magic_name__      				=    (nb_step - 1) * self.chunk_stride + self.chunk_length
                  elif padding:
                         __magic_name__      				=    max(array.shape[0] for array in raw_audio )
                         __magic_name__      				=    int(np.ceil(max_length / self.chunk_stride ) )
                         __magic_name__      				=    (nb_step - 1) * self.chunk_stride + self.chunk_length
                         __magic_name__      				=    'max_length'
                  else:
                         __magic_name__      				=    input_values

        # normal padding on batch
           if padded_inputs is None:
                  __magic_name__      				=    self.pad(
                      UpperCamelCase_				,    max_length=UpperCamelCase_				,    truncation=UpperCamelCase_				,    padding=UpperCamelCase_				,    return_attention_mask=UpperCamelCase_				,    )
                  if padding:
                         __magic_name__      				=    padded_inputs.pop('attention_mask' )

           __magic_name__      				=    []
           for example in padded_inputs.pop('input_values' ):
                  if self.feature_size == 1:
                         __magic_name__      				=    example[..., None]
                  input_values.append(example.T )

           __magic_name__      				=    input_values
           if return_tensors is not None:
                  __magic_name__      				=    padded_inputs.convert_to_tensors(UpperCamelCase_ )

           return padded_inputs